This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The overall aim of this project is to develop and build on our earlier funded work on the segmentation and spatial normalisation for regional morphometry at 1.5T, to make use of novel new imaging contrasts becoming available in higher field imaging. Aim 1: Multi-Sequence Fusion and High Resolution Tissue Segmentation: Development of automated tissue segmentation methods which estimate a voxel by voxel map of both the fractional occupancy and the probability of tissue types from multi-acquisition variable T1 weighted imaging data. This willincorporate inter-frame co-alignment, motion frame rejection, intensity inhomogeneity correction of the frames, and co-alignment and fusion of this data with other MRI sequences (such as DTI and FLAIR) acquired in a typical study. Aim 2: Improved Whole Brain Spatial Normalisation: Develop and refine multi-sequence fine-scalespatial normalisation methods for novel image contrasts, which make use of multiple-contrasts at high isotropic resolution. This will employ large scale diffeomorphic and symmetric image registration driven by robust regionalized entropy based registration criteria. We will also construct and use a high-resolution target template for spatial normalisation. Aim 3: High Resolution Map of T1 values in the Aging Adult Brain: Construction of a population based statistical map capturing the expected T1 values and their variance with location over a set of normal control subjects using the tissue segmentation and spatial normalisation methods developed in aims 1 and 2, together with the acquisition and reconstruction methods developed in this resource proposal. This statistical map will be published on the web as a resource for other researchers to make use of.